Modernize your approach to big data

From mindsets to data sets, make small changes for big results

The big data conversation has invaded almost every corner of the organization. With it comes an urgent expectation for knowledge.

Business leaders have come to expect instant and accurate information sources that provide insight into everything from corporate risk to customer behavior. Of course, the path from big data to knowledge is not always that simple.

How can you simplify and accelerate the spread of knowledge throughout your organization? You can do this by modernizing your approach to big data, with a strategy that meets the needs of both business and technical innovators.

I propose a three-step process that ranges from mindsets to data sets and helps your organization become smarter through the use of big data, analytics and visualization. You need to change on three levels:

Evolve your mindsets.

Balance process with agility.

Leverage those data sets.

This is not a pitch for new technologies. In fact, the first two steps are devoid of any technology changes at all. In many cases, the technology upgrades are already happening. Rather, this advice is to help you get the most out of the changes that are already happening in an environment where Hadoop and other big data technologies are gaining traction.

When you modernize with these technologies, you’re helping to ensure that analytics is always available when it’s needed. And you’re setting up an analytics infrastructure that will grow as users and processing needs continue to increase.

Evolve your mindsets

A recent Harvard Business Review article by Aaron Levie, “How to Compete When IT Is Abundant,”1 asked readers to consider how IT has moved from a scarce resource to an abundant one. What if we changed our mindsets to reflect that change?

In the scarce way of thinking, technology is fraught with constraints, the focus is on cost control, and all technology decisions are process-centric. The bottom line of the scarcity mindset for IT is that everything is forbidden unless it is permitted.

Does that sound familiar? What if you were to have a more abundant mindset regarding technology? What if you empowered users with new technologies, focused on value and discovery, and fostered creativity within the IT process? The bottom line with an abundant IT mindset is that everything is permitted unless it is forbidden.

Consider the implications of those changes. Your business and IT teams become more open and collaborative, and they build knowledge digitally every day. The CIO becomes a technology guide and adviser for the business, not an authoritarian figure.

With the right mindset, the support of the right people, and cultural acceptance, you can start the journey toward new approaches for business knowledge.

The journey, however, is not complete, and should start with an assessment of your current processes. This will help get you further along the path of integrating business and analytical processes.

Balance process with agility

To bridge the improvements from your mindset to your data sets, focus on business and IT alignment. CIOs and their senior leadership teams should champion a new model of collaboration with their key internal business partners and support functions. This collaborative approach is designed to get all the key stakeholders at the table to talk about how to better align technology investment priorities with the critical business outcomes.

If possible, create a data advisory board or steering team to support the process changes. Give everyone a chance to participate and review how to make the operational processes with IT agile to support business partners quickly and systematically.

Next, look for improvements at each stage of the analytic life cycle, from data preparation and data exploration to model development and deployment.

You can modernize the entire life cycle with an “analytics factory” approach. A predictive analytics factory formalizes ongoing processes for analytic data preparation, model building, model management and deployment – with particular attention to the process of managing models.

With the demand rising for big data solutions, a structured approach helps you deploy analytics solutions in a minimal amount of time. This approach standardizes processes and streamlines the management and deployment of the best models so the business can quickly get access to the best answers.

The extra structure up front allows everyone to be more agile and collaborative when it comes to actually using your data to make decisions.

Leverage those data sets

Only after you change your mindsets and your processes is it time to look at your data sets. Do you have the right technology infrastructure in place to deliver valuable information across the enterprise?

Many organizations have already started storing data in Hadoop or other modern architectures for big data. But are you treating data as an asset? Are you storing and analyzing data in the most efficient way possible for analytics?

To really leverage your data sets, consider how you can take small steps to evolve to newer technologies or upgrade the latest versions of your existing tools. Taking advantage of big data concepts and features can turn your existing data into a more valuable asset.

For example, grid computing, cloud computing, in-memory processing and data visualization are four technologies that can help you do more with the data you already have, by improving your processing speeds, giving more users access to analytics and making it possible to solve large, complex problems.

How do they work? A grid server decides when jobs are run based on which machines are open and available. Cloud computing reduces deployment times and support costs. In-memory processing reduces data movement and spreads processing workloads to provide answers at least 100 times faster. And visual analytics technologies make it possible for executives to analyze big data without involving IT. Even advanced analyses – like scenario analysis, decision trees, network diagrams and text analysis – are possible with visual analytics.

When you modernize with these technologies, you’re helping to ensure that analytics is always available when it’s needed. And you’re setting up an analytics infrastructure that will grow as users and processing needs continue to increase.

Modernizing in these three areas – mindsets, processes and data sets – will give decision makers faster, easier and more agile access to data and help bring the best answers to business units sooner.

Tony Hamilton is a Global Product Marketing Principle for Data Management at SAS. He has a background in software solution technologies and IT systems architecture and has a special interest in the evolution of transactional processing methods. Hamilton believes analytics and the management of data processes are as important to business and society as the invention of the Internet. He frequently speaks at industry events about ways to orchestrate data analysis, data management and data visualization for optimal business results.

Hamilton holds a BSC Degree from the University of Strathclyde in Scotland. He recently served on the Board of Advisors for the International Institute for Analytics with Tom Davenport and Jack Phillips.